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Based on the binary time series data of social infection dynamics, we proposea general framework to reconstruct 2-simplex complexes with two-body andthree-body interactions by combining the maximum likelihood estimation instatistical inference and introducing the expectation maximization. In order toimprove the code running efficiency, the whole algorithm adopts vectorizationexpression. Through the inference of maximum likelihood estimation, thevectorization expression of the edge existence probability can be obtained, andthrough the probability matrix, the adjacency matrix of the network can beestimated. We apply a two-step scheme to improve the effectiveness of networkreconstruction while reducing the amount of computation significantly. Theframework has been tested on different types of complex networks. Among them,four kinds of networks can obtain high reconstruction effectiveness. Besides,we study the influence of noise data or random interference and prove therobustness of the framework, then the effects of two kinds of hyper-parameterson the experimental results are tested. Finally, we analyze which type ofnetwork is more suitable for this framework, and propose methods to improve theeffectiveness of the experimental results.

Expectation-Maximizing Network Reconstruction and MostApplicable Network Types Based on Binary Time Series Data